docs: def make_scores(
          ic50_y,
          ic50_y_pred,
          sample_weight=None,
          threshold_nm=500,
          max_ic50=50000):
      """
      Calculate AUC, F1, and Kendall Tau scores.
  
      Parameters
      -----------
      ic50_y : float list
          true IC50s (i.e. affinities)
  
      ic50_y_pred : float list
          predicted IC50s
  
      sample_weight : float list [optional]
  
      threshold_nm : float [optional]
  
      max_ic50 : float [optional]
  
      Returns
      -----------
      dict with entries "auc", "f1", "tau"
      """
  
      y_pred = from_ic50(ic50_y_pred, max_ic50)
      try:
          auc = sklearn.metrics.roc_auc_score(
              ic50_y <= threshold_nm,
              y_pred,
              sample_weight=sample_weight)
      except ValueError as e:
          logging.warning(e)
          auc = numpy.nan
      try:
          f1 = sklearn.metrics.f1_score(
              ic50_y <= threshold_nm,
              ic50_y_pred <= threshold_nm,
              sample_weight=sample_weight)
      except ValueError as e:
          logging.warning(e)
          f1 = numpy.nan
      try:
          tau = scipy.stats.kendalltau(ic50_y_pred, ic50_y)[0]
      except ValueError as e:
          logging.warning(e)
          tau = numpy.nan
  
      return dict(
          auc=auc,
          f1=f1,
          tau=tau)